Neural Network Based Attack on a Masked Implementation of AES

Richard Gilmore, Neil Hanley, Maire O'Neill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

108 Citations (Scopus)


Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.
Original languageEnglish
Title of host publication2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST),
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781467374217
Publication statusPublished - 07 May 2015
EventIEEE International Symposium on Hardware-Oriented Security and Trust (HOST) - , United Kingdom
Duration: 05 May 201507 May 2015


ConferenceIEEE International Symposium on Hardware-Oriented Security and Trust (HOST)
Country/TerritoryUnited Kingdom


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